Sensor-based evaluation of Circadian motor behavior in people with Dementia. Development and validation of analysis strategies

[ITA]La demenza consiste nel deterioramento, spesso progressivo, dello stato cognitivo di un individuo. Chi è affetto da demenza, presenta alterazioni a livello cognitivo, comportamentale e motorio, ad esempio compiendo gesti ossessivi, ripetitivi, senza uno scopo preciso. La condizione dei pazienti affetti da demenza è valutata clinicamente tramite apposite scale e le informazioni relative al comportamento vengono raccolte intervistando chi se ne occupa, come familiari, il personale infermieristico o il medico curante. Spesso queste valutazioni si rivelano inaccurate, possono essere fortemente influenzate da considerazioni soggettive, e sono dispendiose in termini di tempo. Si ha quindi l'esigenza di disporre di metodiche oggettive per valutare il comportamento motorio dei pazienti e le sue alterazioni patologiche; i sensori inerziali indossabili potrebbero costituire una valida soluzione, per questo scopo. L'obiettivo principale della presente attività di tesi è stato definire e implementare un software per una valutazione oggettiva, basata su sensori, del pattern motorio circadiano, in pazienti affetti da demenza ricoverati in un'unità di terapia a lungo termine, che potrebbe evidenziare differenze nei sintomi della malattia che interessano il comportamento motorio, come descritto in ambito clinico. Lo scopo secondario è stato quello di verificare i cambiamenti motori pre- e post-intervento in un sottogruppo di pazienti, a seguito della somministrazione di un programma sperimentale di intervento basato su esercizi fisici. --------------- [ENG]Dementia involves deterioration, often progressive, of a person's cognitive status. Those who suffer from dementia, present alterations in cognitive and motor behavior, for example performing obsessive and repetitive gestures, without a purpose. The condition of patients suffering from dementia is clinically assessed by means of specific scales and information relating to the behavior are collected by interviewing caregivers, such as the family, nurses, or the doctor. Often it turns out that these are inaccurate assessments that may be heavily influenced by subjective evaluations and are costly in terms of time. Therefore, there is the need for objective methods to assess the patients' motor behavior and the pathological changes; wearable inertial sensors may represent a viable option, so this aim. The main objective of this thesis project was to define and implement a software for a sensor-based assessment of the circadian motor pattern in patients suffering from dementia, hospitalized in a long-term care unit, which could highlight differences in the disease symptoms affecting the motor behavior, as described in the clinical setting. The secondary objective was to verify pre- and post-intervention changes in the motor patterns of a subgroup of patients, following the administration of an experimental program of intervention based on physical exercises.

Shoulder kinematic evaluation in patients with rotator cuff tears using inertial and magnetic sensors.

Questo progetto di tesi fa parte di un più ampio studio clinico condotto all’interno dell’azienda NCS Lab (Carpi,(MO)), in collaborazione con il Dr. Claudio Chillemi (ICOT, Latina (RM)) che mira ad eseguire un confronto tra diverse tecniche chirurgiche per la riparazione della cuffia dei rotatori. Lo studio clinico in questione durerà circa due anni: per questo motivo i dati analizzati in questo progetto di tesi provengono solo dal gruppo di pazienti acquisiti nella fase preoperatoria. Tutti i dati sono stati acquisiti utilizzando i sensori magneto-inerziali WISE (tecnologia proprietaria dell’azienda NCS Lab). Questo lavoro di tesi si propone, quindi, di valutare la ripetibilità del movimento in termini di coefficiente di correlazione multipla e di estrapolare alcuni parametri di interesse clinico come, ad esempio, i range di movimento (ROM) della scapola e dell’omero e il ritmo scapolo-omerale (SHR). Questi parametri sono stati poi caratterizzati da un punto di vista statistico al fine di valutare le differenze tra arto patologico e controlaterale. Sono state calcolate, inoltre, le prediction bands con lo scopo di descrivere le differenze tra arto patologico e controlaterale nella coordinazione scapolo-omerale dei pazienti. Per quanto riguarda la ripetibilità del movimento, i risultati ottenuti in questo lavoro di tesi mostrano che la rotazione medio-laterale è caratterizzata da un eccellente CMC sia per l'arto patologico che per il controlaterale. Inoltre, sono state riscontrate differenze significative dal punto di vista statistico tra le distribuzioni dei range di movimento dell'arto patologico e controlaterale. Tali differenze sono state trovate anche per quanto riguarda il ritmo scapolo-omerale.

Implementation of a VLSI amplifier interfaced with biomedical sensors for ultra wide band data transmission

This thesis presents a CMOS Amplifier with High Common Mode rejection designed in UMC 130nm technology. The goal is to achieve a high amplification factor for a wide range of biological signals (with frequencies in the range of 10Hz-1KHz) and to reject the common-mode noise signal. It is here presented a Data Acquisition System, composed of a Delta-Sigma-like Modulator and an antenna, that is the core of a portable low-complexity radio system; the amplifier is designed in order to interface the data acquisition system with a sensor that acquires the electrical signal. The Modulator asynchronously acquires and samples human muscle activity, by sending a Quasi-Digital pattern that encodes the acquired signal. There is only a minor loss of information translating the muscle activity using this pattern, compared to an encoding technique which uses astandard digital signal via Impulse-Radio Ultra-Wide Band (IR-UWB). The biological signals, needed for Electromyographic analysis, have an amplitude of 10-100μV and need to be highly amplified and separated from the overwhelming 50mV common mode noise signal. Various tests of the firmness of the concept are presented, as well the proof that the design works even with different sensors, such as Radiation measurement for Dosimetry studies.

Description and Prediction of Freezing of Gait in Parkinson's Disease from Wearable Inertial Measurements Units

Parkinson's disease (PD) is a neuro-degenerative disorder, the second most common after Alzheimer's disease. After diagnosis, treatments can help to relieve the symptoms, but there is no known cure for PD. PD is characterized by a combination of motor and no-motor dysfunctions. Among the motor symptoms there is the so called Freezing of Gait (FoG). The FoG is a phenomenon in PD patients in which the feet stock to the floor and is difficult for the patient to initiate movement. FoG is a severe problem, since it is associated with falls, anxiety, loss of mobility, accidents, mortality and it has substantial clinical and social consequences decreasing the quality of life in PD patients. Medicine can be very successful in controlling movements disorders and dealing with some of the PD symptoms. However, the relationship between medication and the development of FoG remains unclear. Several studies have demonstrated that visual or auditory rhythmical cuing allows PD patients to improve their motor abilities. Rhythmic auditory stimulation (RAS) was shown to be particularly effective at improving gait, specially with patients that manifest FoG. While RAS allows to reduce the time and the effects of FoGs occurrence in PD patients after the FoG is detected, it can not avoid the episode due to the latency of detection. An improvement of the system would be the prediction of the FoG. This thesis was developed following two main objectives: (1) the finding of specifics properties during pre FoG periods different from normal walking context and other walking events like turns and stops using the information provided by the inertial measurements units (IMUs) and (2) the formulation of a model for automatically detect the pre FoG patterns in order to completely avoid the upcoming freezing event in PD patients. The first part focuses on the analysis of different methods for feature extraction which might lead in the FoG occurrence.

Study of a pulsed LED set-up to characterise the light response of SiPM sensors for the EIC dRICH detector

L’Electron Ion Collider (EIC) è un futuro acceleratore di particelle che ha l’obiettivo di approfondire le nostre conoscenze riguardo l’interazione forte, una delle quattro interazioni fondamentali della natura, attraverso collisioni di elettroni su nuclei e protoni. L’infrastruttura del futuro detector comprende un sistema d’identificazione basato sull’emissione di luce Cherenkov, un fenomeno che permette di risalire alla massa delle particelle. Una delle configurazioni prese in considerazione per questo sistema è il dual-radiator RICH, basato sulla presenza di due radiatori all’esterno dei quali si trovano dei fotorivelatori. Un’opzione per questi sensori sono i fotorivelatori al silicio SiPM, oggetto di questo lavoro di tesi. L’obiettivo dell’attività è lo studio di un set-up per la caratterizzazione della risposta di sensori SiPM a basse temperature, illuminati attraverso un LED. Dopo un’analisi preliminare per determinare le condizioni di lavoro, si è trovato che la misura è stabile entro un errore del 3.5%.

Design of a wearable platform for PPG analysis with multicore low-power processor

Photoplethysmography (PPG) sensors allow for noninvasive and comfortable heart-rate (HR) monitoring, suitable for compact wearable devices. However, PPG signals collected from such devices often suffer from corruption caused by motion artifacts. This is typically addressed by combining the PPG signal with acceleration measurements from an inertial sensor. Recently, different energy-efficient deep learning approaches for heart rate estimation have been proposed. To test these new solutions, in this work, we developed a highly wearable platform (42mm x 48 mm x 1.2mm) for PPG signal acquisition and processing, based on GAP9, a parallel ultra low power system-on-chip featuring nine cores RISC-V compute cluster with neural network accelerator and 1 core RISC-V controller. The hardware platform also integrates a commercial complete Optical Biosensing Module and an ARM-Cortex M4 microcontroller unit (MCU) with Bluetooth low-energy connectivity. To demonstrate the capabilities of the system, a deep learning-based approach for PPG-based HR estimation has been deployed. Thanks to the reduced power consumption of the digital computational platform, the total power budget is just 2.67 mW providing up to 5 days of operation (105 mAh battery).

Methodologies for attitude determination from MEMS sensors in vehicles

The focus of the thesis is the application of different attitude’s determination algorithms on data evaluated with MEMS sensor using a board provided by University of Bologna. MEMS sensors are a very cheap options to obtain acceleration, and angular velocity. The use of magnetometers based on Hall effect can provide further data. The disadvantage is that they have a lot of noise and drift which can affects the results. The different algorithms that have been used are: pitch and roll from accelerometer, yaw from magnetometer, attitude from gyroscope, TRIAD, QUEST, Magdwick, Mahony, Extended Kalman filter, Kalman GPS aided INS. In this work the algorithms have been rewritten to fit perfectly with the data provided from the MEMS sensor. The data collected by the board are acceleration on the three axis, angular velocity on the three axis, magnetic fields on the three axis, and latitude, longitude, and altitude from the GPS. Several tests and comparisons have been carried out installing the electric board on different vehicles operating in the air and on ground. The conclusion that can be drawn from this study is that the Magdwich filter is the best trade-off between computational capabilities required and results obtained. If attitude angles are obtained from accelerometers, gyroscopes, and magnetometer, inconsistent data are obtained for cases where high vibrations levels are noticed. On the other hand, Kalman filter based algorithms requires a high computational burden. TRIAD and QUEST algorithms doesn’t perform as well as filters.

RED-Bridge: A Multiprotocol Tool Prototype for IoT Sensors

The IoT is growing more and more each year and is becoming so ubiquitous that it includes heterogeneous devices with different hardware and software constraints leading to an highly fragmented ecosystem. Devices are using different protocols with different paradigms and they are not compatible with each other; some devices use request-response protocols like HTTP or CoAP while others use publish-subscribe protocols like MQTT. Integration in IoT is still an open research topic. When handling and testing IoT sensors there are some common task that people may be interested in: reading and visualizing the current value of the sensor; doing some aggregations on a set of values in order to compute statistical features; saving the history of the data to a time-series database; forecasting the future values to react in advance to a future condition; bridging the protocol of the sensor in order to integrate the device with other tools. In this work we will show the working implementation of a low-code and flow-based tool prototype which supports the common operations mentioned above, based on Node-RED and Python. Since this system is just a prototype, it has some issues and limitations that will be discussed in this work.

Development of wearable electrochemical sensors for the determination of biological analytes

Wearable biosensors are attracting interest due to their potential to provide continuous, real-time physiological information via dynamic, non-invasive measurements of biochemical markers in biofluids, such as interstitial fluid (ISF). One notable example of their applications is for glycemic monitoring in diabetic patients, which is typically carried out either by direct measurement of blood glucose via finger pricking or by wearable sensors that can continuously monitor glucose in ISF by sampling it from below the skin with a microneedle. In this context, the development of a new and minimally invasive multisensing tattoo-based platform for the monitoring of glucose and other analytes in ISF extracted through reverse iontophoresis in proposed by the GLUCOMFORT project. This elaborate describes the in-vitro development of flexible electrochemical sensors based on inkjet-printed PEDOT:PSS and metal inks that are capable of determining glucose and chloride at biologically relevant concentrations, making them good candidates for application in the GLUCOMFORT platform. In order to make PEDOT:PSS sensitive to glucose at micromolar concentrations, a biocompatible functionalization based on immobilized glucose oxidase and electrodeposited platinum was developed. This functionalization was successfully applied to bulk and flexible amperometric devices, the design of which was also optimized. Using the same strategy, flexible organic electrochemical transistors (OECTs) for glucose sensing were also made and successfully tested. For the sensing of chloride ions, an organic charge-modulated field-effect transistor (OCMFET) featuring a silver/silver chloride modified floating gate electrode was developed and tested.

Private sensors and crowdsourced rainfall data: accuracy and effectiveness for monitoring severe events and mapping pluvial flooding in urban areas

The increasing number of extreme rainfall events, combined with the high population density and the imperviousness of the land surface, makes urban areas particularly vulnerable to pluvial flooding. In order to design and manage cities to be able to deal with this issue, the reconstruction of weather phenomena is essential. Among the most interesting data sources which show great potential are the observational networks of private sensors managed by citizens (crowdsourcing). The number of these personal weather stations is consistently increasing, and the spatial distribution roughly follows population density. Precisely for this reason, they perfectly suit this detailed study on the modelling of pluvial flood in urban environments. The uncertainty associated with these measurements of precipitation is still a matter of research. In order to characterise the accuracy and precision of the crowdsourced data, we carried out exploratory data analyses. A comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national weather services is presented. The crowdsourced stations have very good skills in rain detection but tend to underestimate the reference value. In detail, the accuracy and precision of crowd- sourced data change as precipitation increases, improving the spread going to the extreme values. Then, the ability of this kind of observation to improve the prediction of pluvial flooding is tested. To this aim, the simplified raster-based inundation model incorporated in the Saferplaces web platform is used for simulating pluvial flooding. Different precipitation fields have been produced and tested as input in the model. Two different case studies are analysed over the most densely populated Norwegian city: Oslo. The crowdsourced weather station observations, bias-corrected (i.e. increased by 25%), showed very good skills in detecting flooded areas.